IDEAS home Printed from https://ideas.repec.org/p/gwc/wpaper/2015-006.html
   My bibliography  Save this paper

A Nonparametric Approach to Identifying a Subset of Forecasters that Outperforms the Simple Average

Author

Listed:
  • Constantin Bürgi

    (The George Washington University)

  • Tara M. Sinclair

    (The George Washington University)

Abstract

Empirical studies in the forecast combination literature have shown that it is notoriously di!cult to improve upon the simple average despite the availability of optimal combination weights. In particular, historical performance-based combination approaches do not select forecasters that improve upon the simple average going forward. This paper shows that this is due to the high correlation among forecasters, which only by chance causes some individuals to have lower root mean squared errors (RMSE) than the simple average. We introduce a new nonparametric approach to eliminate forecasters who perform well based purely on chance as well as poor performers. This leaves a subset of forecasters with better performance in subsequent periods. It improves upon the simple average in the SPF for bond yields where some forecasters may be more likely to have specialized knowledge.

Suggested Citation

  • Constantin Bürgi & Tara M. Sinclair, 2015. "A Nonparametric Approach to Identifying a Subset of Forecasters that Outperforms the Simple Average," Working Papers 2015-006, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    • Handle: RePEc:gwc:wpaper:2015-006
    as

    Download full text from publisher

    File URL: https://www2.gwu.edu/~forcpgm/2015-006.pdf
    File Function: First version, 2015
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Anne Sofie Jore & James Mitchell & Shaun P. Vahey, 2010. "Combining forecast densities from VARs with uncertain instabilities," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 621-634.
    2. Antonello D’Agostino & Kieran Mcquinn & Karl Whelan, 2012. "Are Some Forecasters Really Better Than Others?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(4), pages 715-732, June.
    3. Neil Ericsson & Erica Reisman, 2012. "Evaluating a Global Vector Autoregression for Forecasting," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 18(3), pages 247-258, August.
    4. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    5. Yongchen Zhao, 2021. "The robustness of forecast combination in unstable environments: a Monte Carlo study of advanced algorithms," Empirical Economics, Springer, vol. 61(1), pages 173-199, July.
    6. Kajal Lahiri & Huaming Peng & Yongchen Zhao, 2017. "Online learning and forecast combination in unbalanced panels," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 257-288, March.
    7. Christian Kascha & Francesco Ravazzolo, 2010. "Combining inflation density forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 231-250.
    8. Davies, Anthony & Lahiri, Kajal, 1995. "A new framework for analyzing survey forecasts using three-dimensional panel data," Journal of Econometrics, Elsevier, vol. 68(1), pages 205-227, July.
    9. Lahiri Kajal & Yang Liu, 2016. "A non-linear forecast combination procedure for binary outcomes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(4), pages 421-440, September.
    10. Gamber, Edward N. & Smith, Julie K. & McNamara, Dylan C., 2014. "Where is the Fed in the distribution of forecasters?," Journal of Policy Modeling, Elsevier, vol. 36(2), pages 296-312.
    11. Capistrán, Carlos & Timmermann, Allan, 2009. "Forecast Combination With Entry and Exit of Experts," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 428-440.
    12. Genre, Véronique & Kenny, Geoff & Meyler, Aidan & Timmermann, Allan, 2013. "Combining expert forecasts: Can anything beat the simple average?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 108-121.
    13. Todd E. Clark & Michael W. McCracken, 2010. "Averaging forecasts from VARs with uncertain instabilities," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 5-29, January.
    14. Lahiri, Kajal & Peng, Huaming & Zhao, Yongchen, 2015. "Testing the value of probability forecasts for calibrated combining," International Journal of Forecasting, Elsevier, vol. 31(1), pages 113-129.
    15. Poncela, Pilar & Rodríguez, Julio & Sánchez-Mangas, Rocío & Senra, Eva, 2011. "Forecast combination through dimension reduction techniques," International Journal of Forecasting, Elsevier, vol. 27(2), pages 224-237, April.
    16. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    17. Geoff Kenny & Thomas Kostka & Federico Masera, 2015. "Density characteristics and density forecast performance: a panel analysis," Empirical Economics, Springer, vol. 48(3), pages 1203-1231, May.
    18. Conflitti, Cristina & De Mol, Christine & Giannone, Domenico, 2015. "Optimal combination of survey forecasts," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1096-1103.
    19. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    20. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
    21. Batchelor, R A, 1990. "All Forecasters Are Equal," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 143-144, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Constantin Rudolf Salomo Bürgi, 2023. "How to deal with missing observations in surveys of professional forecasters," Journal of Applied Economics, Taylor & Francis Journals, vol. 26(1), pages 2185975-218, December.
    2. Fernando Faure & Carlos A. Medel, 2020. "Does the Exposure to the Business Cycle Improve Consumer Perceptions for Forecasting? Microdata Evidence from Chile," Working Papers Central Bank of Chile 888, Central Bank of Chile.
    3. Qian, Yilin & Thompson, Ryan & Vasnev, Andrey L, 2022. "Global combinations of expert forecasts," Working Papers BAWP-2022-02, University of Sydney Business School, Discipline of Business Analytics.
    4. Diebold, Francis X. & Shin, Minchul, 2019. "Machine learning for regularized survey forecast combination: Partially-egalitarian LASSO and its derivatives," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1679-1691.
    5. Constantin Bürgi & Tara M. Sinclair, 2021. "What does forecaster disagreement tell us about the state of the economy?," Applied Economics Letters, Taylor & Francis Journals, vol. 28(1), pages 49-53, January.
    6. Thompson, Ryan & Qian, Yilin & Vasnev, Andrey L., 2024. "Flexible global forecast combinations," Omega, Elsevier, vol. 126(C).
    7. Yongchen Zhao, 2021. "The robustness of forecast combination in unstable environments: a Monte Carlo study of advanced algorithms," Empirical Economics, Springer, vol. 61(1), pages 173-199, July.
    8. Tim Köhler & Jörg Döpke, 2023. "Will the last be the first? Ranking German macroeconomic forecasters based on different criteria," Empirical Economics, Springer, vol. 64(2), pages 797-832, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Constantin Rudolf Salomo Bürgi, 2023. "How to deal with missing observations in surveys of professional forecasters," Journal of Applied Economics, Taylor & Francis Journals, vol. 26(1), pages 2185975-218, December.
    2. Constantin Burgi, 2015. "Can A Subset Of Forecasters Beat The Simple Average In The Spf?," Working Papers 2015-001, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    3. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    4. Constantin Burgi, 2016. "What Do We Lose When We Average Expectations?," Working Papers 2016-013, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    5. Conflitti, Cristina & De Mol, Christine & Giannone, Domenico, 2015. "Optimal combination of survey forecasts," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1096-1103.
    6. Knut Are Aastveit & Karsten R. Gerdrup & Anne Sofie Jore & Leif Anders Thorsrud, 2014. "Nowcasting GDP in Real Time: A Density Combination Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(1), pages 48-68, January.
    7. Li, Li & Kang, Yanfei & Li, Feng, 2023. "Bayesian forecast combination using time-varying features," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1287-1302.
    8. Diebold, Francis X. & Shin, Minchul, 2019. "Machine learning for regularized survey forecast combination: Partially-egalitarian LASSO and its derivatives," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1679-1691.
    9. Diebold, Francis X. & Shin, Minchul & Zhang, Boyuan, 2023. "On the aggregation of probability assessments: Regularized mixtures of predictive densities for Eurozone inflation and real interest rates," Journal of Econometrics, Elsevier, vol. 237(2).
    10. Geoff Kenny & Thomas Kostka & Federico Masera, 2015. "Can Macroeconomists Forecast Risk? Event-Based Evidence from the Euro-Area SPF," International Journal of Central Banking, International Journal of Central Banking, vol. 11(4), pages 1-46, December.
    11. Kajal Lahiri & Huaming Peng & Xuguang Simon Sheng, 2022. "Measuring Uncertainty of a Combined Forecast and Some Tests for Forecaster Heterogeneity," Advances in Econometrics, in: Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling, volume 43, pages 29-50, Emerald Group Publishing Limited.
    12. Victor Lopez-Perez, 2016. "Macroeconomic Forecast Uncertainty In The Euro Area," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 11(1), pages 9-41, March.
    13. Pilar Poncela & Eva Senra, 2017. "Measuring uncertainty and assessing its predictive power in the euro area," Empirical Economics, Springer, vol. 53(1), pages 165-182, August.
    14. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    15. Graham Elliott, 2017. "Forecast combination when outcomes are difficult to predict," Empirical Economics, Springer, vol. 53(1), pages 7-20, August.
    16. Björn Fastrich & Peter Winker, 2014. "Combining Forecasts with Missing Data: Making Use of Portfolio Theory," Computational Economics, Springer;Society for Computational Economics, vol. 44(2), pages 127-152, August.
    17. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    18. Mauro Costantini & Ulrich Gunter & Robert M. Kunst, 2017. "Forecast Combinations in a DSGE‐VAR Lab," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(3), pages 305-324, April.
    19. Knut Are Aastveit & Francesco Ravazzolo & Herman K. van Dijk, 2018. "Combined Density Nowcasting in an Uncertain Economic Environment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 131-145, January.
    20. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.

    More about this item

    Keywords

    Forecast combination; Forecast evaluation; Multiple model comparisons; Real-time data; Survey of Professional Forecasters;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gwc:wpaper:2015-006. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: GW Economics Department (email available below). General contact details of provider: https://edirc.repec.org/data/pfgwuus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.